Deep Learning-Based Locally Low-Rank Enforced Reconstruction for Accelerated Water-Fat Separation.

Motivation: Multi-contrast acquisitions are the basis for accurate water-fat separation. For fat quantification in the liver, insufficient SNR and long acquisition times are main confounding factors. Goal(s): Provide enhanced image quality of individual contrast images to allow water-fat separation using conventional algorithms for accelerated acquisitions. Approach: Joint reconstruction of multiple contrasts using a deep learning-based reconstruction that performs regularization in a locally transformed contrast domain. Results: The proposed method yielded contrasts with PSNR = 34.85 dB and SSIM = 0.94 , showcasing its superiority over the conventional reconstruction technique (PSNR = 31.28, SSIM = 0.86) when applied to a challenging low-field MRI scenario. Impact: Combining iterative DL-based reconstruction with LLR regularization not only allows to accelerate multi-contrast acquisitions but also yields images with high SNR for accurate fat fraction quantification. The approach has the potential to translate established liver fat quantification to low-field MRI.